SAMM: A Spontaneous Micro-Facial Movement Dataset

Adrian Keith Davison, Cliff Lansley, Nicholas Costen, Kevin Tan, Moi Hoon Yap

Abstract:

Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but
attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity,
however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous microexpressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous microfacial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses
the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was
completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a
selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference
analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micromovement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes
show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.